
It should be noted that \(C\)  in this setting is no longer the sampled subset of episode categories, but rather the entire set of training classes (e.g., \(|C|=64\)  for mini-ImageNet).
We then use this output probability distribution to calculate the standard cross-entropy classification loss. During the pre-training stage, we fix \(\alpha =\beta =0\)  but keep \(\gamma \)  a learnable parameter. After pre-training is finished, all learned matrices \(\lbrace M_c|c\in C\rbrace \)  are discarded (similar to the pre-trained MLP classifier in [1]}, [2]}, [3]}, [4]}, [5]}). The pre-trained model size is thus the same as when trained from scratch.
